Evaluation of Pathologic Complete Response (pCR) to Neoadjuvant Chemotherapy in Iranian Breast Cancer Patients with Estrogen Receptor Positive and HER2 Negative and impact of predicting variables on pCR

CD10 expression as a potential predictor of pathological complete response in ER-negative and triple-negative breast cancer patients treated with anthracycline-based neoadjuvant chemotherapy

BACKGROUND

Neoadjuvant chemotherapy (NCT) can induce a pathological complete response (pCR) in breast cancer patients, leading to improved outcomes. However, predicting which patients will achieve pCR remains a challenge. CD10, a myoepithelial marker, has shown diagnostic and prognostic value in metastatic tumors. Its potential as a predictor of chemosensitivity to anthracycline-based NCT in breast cancer is unknown.

AIM

This retrospective study aimed to investigate the potential of CD10 cancer cell expression as a predictive marker of chemosensitivity in breast cancers treated with anthracycline-based neoadjuvant chemotherapy.

METHODS

We analyzed 130 patients with invasive ductal carcinoma who received anthracycline-based NCT. CD10 expression was assessed by immunohistochemistry on pre-treatment biopsies. Statistical analysis evaluated the association between CD10 expression and pCR rates.

RESULTS

Univariate analysis revealed that ER-positive and CD10-negative tumors had lower pCR rates [OR 7.4830 (95% CI 2.7762-20.1699); p = 0.0001]. Multivariate analysis confirmed ER status as a strong predictor of poor response [OR 0.085 (95% CI 0.024-0.30); p < 0.001] and CD10 expression as a predictor of a favourable response [OR 0.11 (0.8-0.19); p = 0.049]. CD10 expression significantly predicted pCR in ER-negative cases [OR 0.1098 (0.0268-0.4503); p = 0.0022] and triple-negative breast cancer [OR 0.0966 (95% CI 0.0270-0.3462); p = 0.0003]. Concordance was observed between core biopsies and excised samples.

CONCLUSION

Positive CD10 cancer cell expression may predict increased response to anthracycline-based neoadjuvant chemotherapy in ER-negative and triple-negative breast cancer cases. Further research is needed to validate these findings in larger cohorts and determine the clinical utility of CD10 as a predictive marker.

Predictive factors for complete pathological response in hormone receptor-negative breast cancer patients undergoing neoadjuvant chemotherapy

Complete pathological response (pCR) is a pivotal predictor of enhanced disease-free and overall survival rates in breast cancer patients. Accurate prediction of pCR is therefore of paramount clinical significance. This retrospective study aimed to delineate the factors associated with pCR through a comprehensive analysis encompassing clinical, pathological, and immunohistochemical profiling of patients diagnosed with hormone receptor-negative invasive ductal carcinomas. The study cohort was composed of 73 female patients. The cases were reviewed retrospectively using data from University Hospital "Tsaritsa Yoanna" in Sofia, spanning the ten-year period from 2010 to 2020. Univariate analyses demonstrated that patients diagnosed with a higher disease stage, specifically stage IIIb, exhibited a notable association with an unfavorable response to neoadjuvant chemotherapy (NCT) [OR 4.5455 (95%CI 1.6810 - 12.2910); p = 0.0029]. Invasive carcinomas containing a ductal carcinoma in situ (DCIS) component [OR 0.3333 (95%CI 0.1226 - 0.9063); p = 0.0313] or were classified as poorly differentiated [OR 0.3056 (95%CI 0.1159 - 0.8055); p = 0.0165] demonstrated an enhanced likelihood of achieving pCR. Tumors expressing CD10 [OR 0.1452 (95%CI 0.0515 - 0.4093); p = 0.0003] and tumors lacking EGFR [OR 3.9722 (95%CI 1.4691 - 10.7399); p = 0.0066] exhibited a markedly elevated rate of pCR. Multivariate regression analysis supported findings. In conclusion, hormone receptor-negative breast tumors stand to benefit from increased pCR rates if they encompass a DCIS component and exhibit CD10 expression while lacking EGFR expression. These findings underscore the importance of comprehensive profiling in predicting pCR outcomes in hormone receptor-negative breast cancer patients undergoing neoadjuvant chemotherapy.

Can peritumoral edema evaluated by Magnetic Resonance Imaging before neoadjuvant chemotherapy predict complete pathological response in breast cancer?

BACKGROUND AND AIMS

The complete pathological response (pCR) following neoadjuvant chemotherapy (NAC) in breast cancer is essential for the accurate prediction of prognosis. We aimed to evaluate the efficacy of the presence and type of peritumoral edema detected by magnetic resonance imaging (MRI) in predicting pCR to NAC in breast cancer patients.

METHODS AND RESULTS

One hundred five patients with the diagnosis of invasive carcinoma were evaluated by MRI before NAC. Edema was evaluated in fat-suppressed T2-weighted images. The patients were categorized into three groups: patients with no peritumoral edema, patients with peritumoral edema, and patients demonstrating subcutaneous edema. The cases were categorized as being pCR and non-pCR. Molecular subtypes, lymphovascular invasion (LVI), tumor size, and apparent diffusion coefficient (ADC) were evaluated. A positive relationship was found between the presence of edema and tumor size. Subcutaneous edema was found to be statistically higher in non-pCR patients. While the number of pCR patients with subcutaneous edema was 17 (30.4%), the number of non-pCR patients with subcutaneous edema was 26 (53.1%) (p  =  0.018). LVI was found to be statistically higher in patients with edema. The number of edema-negative and LVI (+) patients was 4 (15.4%), while the number of edema-positive and LVI (+) patients was 28 (35.4%) (p = 0.042). Intratumoral and peritumoral ADC values were significantly higher in tumors with edema.

CONCLUSION

The presence of subcutaneous edema and LVI may be utilized for the prediction of pCR outcomes in breast cancer patients scheduled for NAC treatment.

Relationship between pathological response and molecular subtypes in locally advanced breast cancer patients receiving neoadjuvant chemotherapy

Majority of patients with breast cancer were diagnosed with locally advanced stages of the disease (54%). This study aimed to explain the pathological response received to neoadjuvant chemotherapy (NACT) according to the molecular classification of breast cancer in patients with locally advanced tumors. One hundred and one patients with locally advanced breast cancer treated with neoadjuvant chemotherapy were analyzed. Patients were classified into five molecular subtypes based on the profile of the estrogen receptor, progesterone receptor, HER2, and Ki-67. We determined associations between complete pathological response (no invasive tumor after neoadjuvant chemotherapy) and molecular subgroups. Most patients had luminal A tumors (n: 28, 27.7%). The overall rate of complete pathological response (pCR) was 34.7% (n:35). Tumors that presented with the highest rate of pCR were pure HER2-positive, at 60% (n:6; OR, 3.2; 95% CI, 0.8-12.2). According to logistic regression analysis, the factors affecting pCR were HER2 positivity and clinically positive axilla before NACT. Luminal A tumors had a significantly lower pCR rate. (7.1%,p: 0.001). Despite the low pCR rate, Luminal A tumor had the best survival rate in the subgroups (p < 0.001). However, there was no difference between EFS and OS according to pCR in any molecular subgroups. Pathological complete response is directly related to the subtypes of breast cancer. A high complete pathological response rate is observed in the pure HER2-positive group. However, EFS and OS were not statistically significant in patients with and without pCR.

Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction

Breast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high heterogeneity of breast tumors and fibro-glandular tissue, which results in lower cancer detection sensitivity and specificity and large inter-reader variability. In order to help overcome these clinical challenges, researchers have made great efforts to develop computer-aided detection and/or diagnosis (CAD) schemes of breast images to provide radiologists with decision-making support tools. Recent rapid advances in high throughput data analysis methods and artificial intelligence (AI) technologies, particularly radiomics and deep learning techniques, have led to an exponential increase in the development of new AI-based models of breast images that cover a broad range of application topics. In this review paper, we focus on reviewing recent advances in better understanding the association between radiomics features and tumor microenvironment and the progress in developing new AI-based quantitative image feature analysis models in three realms of breast cancer: predicting breast cancer risk, the likelihood of tumor malignancy, and tumor response to treatment. The outlook and three major challenges of applying new AI-based models of breast images to clinical practice are also discussed. Through this review we conclude that although developing new AI-based models of breast images has achieved significant progress and promising results, several obstacles to applying these new AI-based models to clinical practice remain. Therefore, more research effort is needed in future studies.

Exploring the influencing factors of the pathologic complete response in estrogen receptor-positive, HER2-negative breast cancer after neoadjuvant chemotherapy: a retrospective study

Background

Pathological complete response (pCR) is the goal of neoadjuvant chemotherapy (NACT). We aimed to develop a nomogram to predict the probability of achieving pCR in estrogen receptor-positive (ER+), HER2-negative (HER2−) breast cancer patients.

Methods

A total of 273 ER+, HER2− breast cancer patients who received 4 cycles of thrice-weekly standard NACT in the First Affiliated Hospital of Chongqing Medical University were retrospectively enrolled. Univariate and multivariate logistic regression analyses were used to screen the predictive factors to develop the nomograms. The discrimination and calibration abilities were assessed by the C-index, receiver operating characteristic curve (AUC), and calibration plot.

Results

There were 28 patients (10.3%) with overall pCR, 38 patients (13.9%) with breast pCR after NACT. ER expression, PgR expression, the neutrophil-to-lymphocyte ratio (NLR) and the Ki-67 index were independent predictive factors for achieving overall pCR. These indicators had good discrimination and calibration ability (AUC 0.843). The nomogram for breast pCR was established based on ER expression, PgR expression, the NLR, and the Ki-67 index and showed great discriminatory ability, with an AUC of 0.810. The calibration curve showed that the predictive ability of the nomogram was a good fit to actual observations.

Conclusion

The nomograms exhibited a sufficient discriminatory ability for predicting pCR after NACT in ER+, HER2− breast cancer patients. Utilizing these nomograms will enable us to identify patients at high probability for pCR after NACT and provide a reference for preoperative adjuvant therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-022-02492-7.